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Genomic alterations in gynecological malignancies: histotype-associated driver mutations, molecular subtyping schemes, and tumorigenic mechanisms.

Seiichi MoriOsamu GotohKazuma KiyotaniSiew-Kee Amanda Low
Published in: Journal of human genetics (2021)
There are numerous histological subtypes (histotypes) of gynecological malignancies, with each histotype considered to largely reflect a feature of the "cell of origin," and to be tightly linked with the clinical behavior and biological phenotype of the tumor. The recent advances in massive parallel sequencing technologies have provided a more complete picture of the range of the genomic alterations that can persist within individual tumors, and have highlighted the types and frequencies of driver-gene mutations and molecular subtypes often associated with these histotypes. Several large-scale genomic cohorts, including the Cancer Genome Atlas (TCGA), have been used to characterize the genomic features of a range of gynecological malignancies, including high-grade serous ovarian carcinoma, uterine corpus endometrial carcinoma, uterine cervical carcinoma, and uterine carcinosarcoma. These datasets have also been pivotal in identifying clinically relevant molecular targets and biomarkers, and in the construction of molecular subtyping schemes. In addition, the recent widespread use of clinical sequencing for the more ubiquitous types of gynecological cancer has manifested in a series of large genomic datasets that have allowed the characterization of the genomes, driver mutations, and histotypes of even rare cancer types, with sufficient statistical power. Here, we review the field of gynecological cancer, and seek to describe the genomic features by histotype. We also will demonstrate how these are linked with clinicopathological attributes and highlight the potential tumorigenic mechanisms.
Keyphrases
  • papillary thyroid
  • high grade
  • copy number
  • single cell
  • squamous cell
  • squamous cell carcinoma
  • single molecule
  • rna seq
  • gene expression
  • risk assessment
  • climate change
  • deep learning
  • human health